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AI Agents for Stock Trading

Course Information

  • Sessions 1 day
  • Duration 7.5 hrs
  • Level Beginner
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Corporate Office Suite, KL Gateway, No. 2, Jalan Kerinchi, Gerbang Kerinchi Lestari, 59200 Kuala Lumpur, Malaysia

Course Brochure

Download Course Brochure

Prerequisite

You should complete AI Powered Quantitative Trading Fundamentals (C728) before taking this course.

C728 demonstrates the multi AI agents trading system and the indicators, backtesting and paper trading it relies on; this course teaches you to build it from scratch.

Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

Build a Multi AI Agents Workflow for Stock Trading

Course Code: C177

9 Review(s) | Add Your Review

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What's This Course About

Build a Multi AI Agents Workflow for Stock Trading teaches you to build, from scratch, the multi AI agents trading system demonstrated in AI Powered Quantitative Trading Fundamentals. Using AI vibe coding, you design eight cooperating agents and direct an AI coding assistant such as Claude Code or OpenAI Codex to write each one, then review the code against a written contract and prove it with tests.

A lead agent orchestrates the flow. The finance research agent gathers Twelve Data candles at 1-minute, 15-minute and 1-hour intervals plus Yahoo Finance and Equibles company data, while the news sentiment agent reads Reddit through the Reddit API and headlines from NewsAPI. The trading strategy agent merges both reports into a strategy built on trend, momentum, volatility, VWAP, RSI, MACD or a mix of them, and the signal generation agent turns it into a long, short or null signal.

The stock trader agent then places the order on an Alpaca paper-trading account, the performance analysis agent reports return, Sharpe ratio, drawdown and win rate on a dashboard, and the strategy review agent studies the results and updates the trading strategy for the next run. You finish by publishing your project to your own GitHub repository. Every lab also runs offline on sample data. All trading is paper trading and nothing in the course is investment advice.

Why Take This Course?

  • Hands-On Learning Learn by doing. Every topic is practised hands-on, so you leave with skills you can use at work straight away.
  • Live Coaching Experienced instructors coach you live, answer your questions and guide you step by step.
  • Free Post-Course Consultation Stuck after class? Message your trainer any time for guidance as you apply what you have learned.
  • Always-Updated Materials Materials are refreshed to stay relevant. Log in to our LMS any time to get the latest version.
HRD Corp Claimable

🏦 HRD Corp Claimable

This course is HRD Corp claimable for eligible employers. Grant approval and reimbursement remain subject to HRD Corp requirements, the employer’s available levy and the approved grant details. How the HRD Corp process works →

Course Fee

MYR1,100.00
+ 8% SST

Course Date

Course fees are shown before SST. An 8% SST is included in the estimated total during registration.

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

Topic 1: Designing a Multi AI Agents Trading Workflow

The Eight Agents and the Lead Agent that Orchestrates Them

The Message Contract between Agents

The AI Vibe Coding Loop: Spec, Prompt, Review, Test, Commit

Lab 1: Scaffold the Project and the Agent Message Contract

Topic 2: Building the Research and Decision Agents

Finance Research Agent: Twelve Data 1min, 15min and 1h Candles, Yahoo Finance and Equibles

News Sentiment Agent: Reddit API and NewsAPI Sentiment

Trading Strategy Agent: Trend, Momentum, Volatility, VWAP, RSI, MACD and Mixed Algorithms

Signal Generation Agent: Long, Short or Null Signals

Labs 2-5: Build the Finance Research, News Sentiment, Trading Strategy and Signal Generation Agents

Topic 3: Execution, Performance and the Feedback Loop

Stock Trader Agent: Automated Orders on an Alpaca Paper Account

Performance Analysis Agent: Return, Sharpe, Drawdown and Win Rate on a Dashboard

Strategy Review Agent: the Feedback Loop that Updates the Strategy

Lead Agent Orchestration and Publishing the Project to GitHub

Labs 6-8: Build the Stock Trader, Performance Analysis, Strategy Review and Lead Agents

Course Info

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

Hardware: Windows PC or Mac laptop

How to Apply HRD Corp Funding

This course is HRD Corp claimable for employers registered with HRD Corp, under the HRD Corp Claimable Courses scheme (SBL-Khas). Your company applies for the grant before the class; we submit the training provider claim after it.

  1. Check your levy balance - your company must be registered with HRD Corp and have enough levy balance to cover the course fee.
  2. Request a quotation - contact us for the quotation, course content and trainer profile; you attach these to the grant application.
  3. Apply for the grant in eTRiS before the class - log in to your employer eTRiS account, go to Application > Grant > Apply Grant, choose the scheme HRD Corp Claimable Courses (SBL-Khas), select us as the training provider and this course, add your trainees, attach the quotation, course content and trainer profile, and submit.
  4. Submit in time - for public classes, submit at least 3 calendar days before the class (15 June to 31 December 2026) and at least 14 days before from 1 January 2027; in-house classes need 14 days. The training must start within 90 days of that advance submission period.
  5. Send us the grant approval - once HRD Corp approves the application, email us the grant ID so we can confirm your seats.
  6. Attend the class - trainees sign the daily attendance (T3) form.
  7. Claim after the class - we submit the training provider claim in eTRiS with the attendance and invoice; you then complete the employer claim and sign the JD/14 form (manager level or above). Claims must be submitted within 6 months of the training.

More details: our HRD Corp Claimable Courses page and the HRD Corp employer guidelines.

Job Roles

Job Roles

  • Retail Investor
  • Stock Trader
  • Active Trader
  • Investment Analyst
  • Equity Research Analyst
  • Portfolio Manager
  • Quantitative Analyst
  • Wealth Manager
  • Remisier and Dealer
  • Finance Professional
  • Data Analyst
  • FinTech Professional

Trainers

Trainers

Dr. Alfred Ang approaches the markets the same way as any engineering problem: with clean data, clear hypotheses and tools that keep the human in charge. As CTO of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses, Dr. Ang works as a full-stack AI developer, Agentic AI solution architect and corporate trainer. Among more than 300 public repositories at github.com/alfredang, the most relevant to this course is n8n-financeadvisor, a dark-themed stock analysis dashboard that pulls live price and volume data through TradingView and TwelveData charts and pairs it with an n8n-powered Telegram AI agent that issues Buy, Sell or Hold views together with its reasoning. Dr. Ang has also built an investment advisor lead-magnet site with an n8n FAQ chatbot, financial planning sites with retirement calculators, and n8n-inventorycontrol, a forecasting agent that runs on a local LLM through Ollama and flags items that cross defined thresholds, a pattern that maps neatly onto price alerts and watchlists. Beyond finance, the portfolio includes RAG chatbots, data dashboards and Streamlit machine-learning labs that teach how to test models against real data. That mix of market data plumbing, agent design and analytical discipline means Dr. Ang can show traders exactly how an AI agent gathers, checks and interprets information, and where its conclusions should still be questioned before any money is committed. Dr. Ang is equally interested in the limits of these tools, including stale data, misleading backtests and agents that sound certain when the evidence is thin.

AI Agents for Stock Trading is built around that discipline. The first topic, from trading idea to quality market data, shows learners how to frame a trading idea as a testable hypothesis, connect an AI agent to reliable price, volume and fundamental data sources, and check that data for gaps, errors and survivorship bias before drawing any conclusion. The second topic introduces a six-step trading workflow and staying in control, in which the agent helps research, analyse, test and summarise, while the learner sets the rules, reviews every recommendation and makes the final decision. Dr. Ang demonstrates each step with working agents, including the Telegram-based analysis bot and dashboards built in-house, then lets participants configure their own agents, write instructions and inspect the reasoning behind each output. The emphasis is on process and risk management rather than tips: documenting assumptions, avoiding over-fitting, keeping a trade journal and recognising when an agent is confidently wrong. Retail investors, active traders and finance professionals will leave with a repeatable research workflow and a clearer understanding of what AI can and cannot do in the markets. The course is educational and does not provide financial advice; every trading decision remains the learner's own. Participants also learn to compare an agent's analysis against independent sources, document the evidence behind each idea and set clear rules for position sizing and exits before any trade is placed. To explore the stock analysis dashboard and agent projects behind this course, visit Dr. Ang's portfolio at https://github.com/alfredang.

Review

Customer Reviews (9)

Engaging trainer Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 17/07/2025)
Very satisfied Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The training was very practical and hands-on. I could apply what I learned to my work immediately. (Posted on 02/02/2025)
Very informative Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 26/07/2024)
Good course materials Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Really enjoyed the training. The examples were relevant and the pace was just right. (Posted on 10/05/2024)

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